PENNY ANN WHEELER, MD President and Chief Clinical Officer, Allina Health

1. Session #16:
How Allina Health Uses Analytics to Transform Care
Penny Ann Wheeler, MD
President and Chief Clinical Officer, Allina Health

2. ADVANCING CARE THROUGH ANALYTICS
THE ALLINA HEALTH JOURNEY
Penny Wheeler, M.D.
President and Chief Clinical Officer
September 2014

3. Key Questions
• Who is Allina Health?
• Why change?
• What are the new measures of success?
• What’s needed to move to higher value care?
• How do we use advanced analytics to drive
improvement?
• What are our results thus far and lessons learned?
3

4. 4

5. Allina is the Region’s Largest
Health Care Organization
• 13 Hospitals
• 82 Clinic sites
• 3 Ambulatory care centers
• Pharmacy, hospice, home
care, medical equipment
• 26,000 employees
• 5,000 physicians
• 2.8 million+ clinic visits
• 110,000+ inpatient hospital
admissions
• 1,658 staffed beds
• 3.4B in revenue
• 32% Twin Cities market
share
5

6. The Imperative for Change:
The Traditional Healthcare Model is Broken
Representative timeline of a patient’s experiences in the U.S. health
care system
http://www.iom.edu/~/media/Files/Activity%20Files/Quality/LearningHealthCare/Release%20Slides.pdf

7. Why Change?
If food prices
had risen at
medical inflation rates
since the 1930s
*Source: American Institute for Preventive Medicine
2009
1 dozen eggs $85.08
1 pound apples $12.97
1 pound sugar $14.53
1 roll toilet paper $25.67
1 dozen oranges $114.47
1 pound butter $108.29
1 pound bananas $17.02
1 pound bacon $129.94
1 pound beef shoulder $46.22
1 pound coffee $68.08
10 Item Total $622.27
7

9. All About Creating Value…
9
Value = Good / Cost
“Quality improvement is the most powerful driver of
cost containment.”
- Michael Porter, PhD Economics
Harvard Business School

10. Preventable Complications
Unnecessary Treatments
Inefficiency
Errors
Services
That
Add
Value
40%
Waste
60%
Value
All Services
Add
Value
100%
Value
Future
Now
What We Pay For…
10

11. Poll Question #1
In your opinion, which of the 4 categories of
waste is the most important to address by the
healthcare industry?
a) Preventable Complications
b) Unnecessary Treatments
c) Inefficiency
d) Errors

12. Four Measures of Success:
Allina Health 2016 Strategic Outcomes
1. Patient Care/Experience
2. Population Health
3. Patient Affordability
4. Organizational Vitality
12
Better
Care/
Experience
Better
Health
Reduce per
capita costs
Organizational Vitality

14. Triple Aim Integration Initiatives
Quality Roadmap
Goal Initiative(s)
1) Perform under payment for quality and
value models
Accountable care pilots
• Pioneer ACO
• Commercial partnerships
2) Align incentives across employed and
affiliated providers
Allina Integrated Medical Network
3) Give providers the data and
information needed to improve
outcomes
Advanced analytics infrastructure
including a robust Enterprise Data
Warehouse (EDW)
4) Provide consistently exceptional care
without waste
• Primary care team model redesign
• Care management/patient engagement
• Clinical program optimization
5) Support transformation with new skills
development
Allina Advanced Training Program

15. Allina Health Enterprise Health Management Platform
Transitioning Data to Actionable Information

16. Bridging Historical, Current, and Predictive Information
Selected Health Intelligence & Delivery Tools at Allina
PPR Dashboard
“Potentially
Preventables”
Census
Dashboard
Enterprise Data
Warehouse
Reporting
Workbench
Retrospective Real time Predictive
What happened? What is happening? What may happen?
General Specific
Readmissions
Model
Modeling of
Potentially
Preventable
Events

17. Poll Question #2
For healthcare providers, on a scale of 1-5,
how well do you feel you are using predictive
information to address potentially preventable
events?
1) No use
2) Just starting or sporadic use
3) Moderate use but increasing
4) Good use
5) Very strong use
6) Unsure or not applicable

18. Example: Supporting Care Coordination
Predicting Unnecessary Admissions and
Readmissions
Challenge
– Substantially reduce unnecessary admissions and readmissions
Solution
– Predict patients at high risk for unnecessary admissions and readmissions
– Develop and use census dashboard to identify and manage patients
– Prioritize care coordination and clinical interventions based on risk level
– Predictive model C-statistic of 0.729
Results
– Reduced readmissions for patients
who received transition
conferences (June 2013-June
2014)
• High-risk patients: 15.8%
decrease in readmissions
• Moderate-high-risk patients:
5.4% decrease in readmissions

19. Getting the Model to the Bedside
The Census Dashboard
Identifies Patient
Readmit Risk
Identifies Transition
Conference Status
Identifies Prior IP Visits
in Last Week & Month

20. 20
Allina Results: Heart Failure
25%
20%
15%
10%
5%
0%
Combined Metro
Combined Metro Linear (Combined Metro)
2011 Q12011 Q22011 Q32011 Q42012 Q12012 Q22012 Q32012 Q42013 Q12013 Q22013 Q32013 Q4

21. RARE Campaign
Graph provided by ICSI
21

22. The Readmission Model Results:
How are our patients grouped?
• High Risk:
– 20 – 100% Readmission Risk: 7% of population
• Moderate-High Risk:
– 10 – 20% Readmission Risk: 19% of population
• Moderate Risk:
– 5 – 10% Readmission Risk: 35% of population
• Low Risk:
– 0 – 5% Readmission Risk: 39% of population
22
0% to 5% 5% to 10%
10% to
15%
15% to
20%
20% to
25%
25% to
35%
35% to
80%
45%
40%
35%
30%
25%
20%
15%
10%
5%
Percent of Total Patients 39% 35% 13% 6% 3% 3% 1%
Percent of total Readmissions 14% 31% 22% 13% 9% 7% 5%
35%
30%
25%
20%
15%
10%
5%
0%
0%
Percent of Total Readmissions
Percent of Total Patients
Model estimated percent probability of readmission

23. Predictive Model Confidence
Why do we believe the Readmission Model?
Comparing existing models with standard C-Statistic (Area under
ROC Curve) measure of performance
– Random coin toss selection: 0.5
– State-of-art techniques(ACG): (0.70 to 0.77)[1]
– Current Allina technique: 0.861
Allina Model was found to have a precision* of ~ 0.9
*Precision is the fraction of Predicted patients that actually have a PPE. In this case, on a dataset in
which it was tested about 90% of patients predicted by the model had a PPE. Note, this is different
from sensitivity, which is the fraction of actual PPE instances that are predicted.
1 Shannon M.E. Murphy, MA, Heather K. Castro, MS, and Martha Sylvia, PhD, MBA, RN, “Predictive Modeling in Practice: Improving the
Participant Identification Process for Care Management Programs Using Condition-Specific Cut Points”, POPULATION HEALTH
MANAGEMENT, Volume 14, Number 0, 2011

24. Example: Basic Cost Curve for Individual
$9,000
$8,000
$7,000
$6,000
$5,000
$4,000
$3,000
$2,000
$1,000
$0
with a Major Hospitalization
-20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Months Before and After High Cost Event
Healthways Data for Diabetics with heart Failure(blue line)
24
Point of traditional payer-based
care management
Point of predictive
intervention
Green: potential cost curve
with predictive intervention

25. Example: Supporting Cohort Management
Providing Care to Patients with Diabetes
Challenge
– Provide superior care for Allina Health’s diabetic population
Solution
– Identified and stratified diabetes cohorts using registries
– Identified gaps in care for diabetes patients (e.g. A1c, blood pressure
management)
– Provided workflow capability for care teams to manage the population
through ambulatory quality dashboard
Results
– Highest national score for Diabetes Care Quality Measure in 2012 of all
CMS Pioneer ACOs
– U.S. leader in management of diabetes patients and Diabetes Optimal
Care results

26. Supporting Cohort Management
Driving Improvement through Access to Information
Select by patient,
clinic, provider or
any combination Filter by Pioneer
Shows performance
of composite measure
components
ACO Patients

27. Example: Supporting Wellness & Prevention
Successfully Keeping Patients Well
Challenge
– Avoiding future illness is core to
superior population health
management
Solution
– Established and reported on
optimal care scores for individuals
– Identified gaps in care and
accurately connected them to care
teams to close gaps in care
Results
– Eliminated significant gaps in
wellness screening and
preventative care
– Allina Health has achieved some
of the best ambulatory optimal
care scores in the nation through a
focused clinician engagement
strategy using the EHMP
Colon Cancer Screening Optimal Care
76.0%
71.0%
66.0%
61.0%
88.0%
86.0%
84.0%
82.0%
80.0%
78.0%
76.0%
74.0%
Mammogram Optimal Care Goal = 85%
Jan-11
Mar-11
May-11
Jul-11
Sep-11
Nov-11
Jan-12
Mar-12
May-12
Jul-12
Sep-12
Nov-12
Jan-13
Mar-13
May-13
Jul-13
56.0%
Colon Cancer Screening Optimal Care Goal = 73%
Mammogram Optimal Care

28. Supporting Wellness & Prevention
Ambulatory Dashboard
MD Name
Ability to focus on a
specific provider or
patient population
Shows performance on
optimal care and component
measures with patient detail,
provider name and clinic

29. Summary
This is only just the start…
Lessons Learned
– Pareto analysis of population data key for determining
opportunity and focus
– Consistent quality drives lower cost of care
• Focus on waste / “unhelpful care variation”
– Use predictive modeling to focus care management
resources
– Strengthen the patient/primary care team relationship
– Keep the patient at the center of all decisions

30. Thank You

31. Transition from Volume to Value
Planning for the inflection point
Payment Type
Penetration
FFS
Global payment
Other
Time
100%
50%
5%
• Retain patients (keepage)
• Regulatory requirements
• Manage risk progression
• Payment reform
• Increase volume
• Maximize payment
• Minimize cost
• Meet regulatory
requirements
Today Transition Tomorrow
Phase
Objectives
• Evolve priorities based on:
• Contracts
• Populations
• Regulatory changes

32. Driving Improvement to Advance Care
The Clinical Program Infrastructure
Clinical Program Infrastructure
Clinical /Operational
Leadership Team
Regional and system
wide physician,
administrative and
clinical operations
leaders needed to
implement
best practice
Information Management Infrastructure
Measurement System
Staff support personnel
and systems necessary
to measure
clinical, financial and
satisfaction
outcomes
for key clinical
processes
Implementation Support
Staff and systems
necessary to develop,
disseminate, support
and maintain
the clinical
knowledge base
necessary to
implement
best practice

33. Translating Concept to Action
Selection of Key Allina Health Initiatives
Allina Integrated Medical (AIM) Network
– Aligns 900+ independent physicians and 1,200 Allina Health employed physicians to
deliver market-leading quality and efficiency in patient care
– Clinical Service Lines (CSLs)
– Provide consistently exceptional and coordinated care across the continuum of care and
across sites of care. CSLs are physician-led, professionally-managed and patient
centered.
Medicare Pioneer ACO
– Member of CMS Pioneer Pilot Demonstration
– Above average performance for 25 of 33 quality performance measures, including the
highest performer for 3 of the measures
– Held the Pioneer ACO Population to 0.8% cost growth for 2012
Northwest Metro Alliance
– A multi-year collaboration between HealthPartners & Allina Health in the Northwest Twin
Cities suburbs focused on the Triple Aim and a learning lab for ACOs
– Since the Alliance model was implemented, medical cost increases have been below the
metro average for the past two years and cost increases were less than one percent for
two years in a row
– Expanded access to stress tests for ED patients with chest pain and prevented 480 low-risk
chest pain inpatient admissions, saving an estimated $2.16 Million in 2012

34. Pioneer ACO
Selected Focus Areas
Area of Focus Implemented Tactics
Preventable
Admissions &
Emergency Department
Visits
• Applied risk stratification to provide outreach and support to patients at risk for preventable
events through Advanced Care Team or Team Care resources
• Outreach to patients who have not been seen, check treatment compliance and schedule visit
• Using After-Visit-Summary instructions during patient follow-up care
• Develop patient-centered goals
• Provide social worker support if needed
• Provide support for Advanced Care Planning
Preventable
Readmissions
• Applied predictive tool to identify patients most at risk for readmission
• Prepare integrated After-Visit-Summary and provide the patient w/a Discharge ‘Packet’
• Provider transitions
• Care transitions intervention
• Determine and leverage role of pharmacist
• Patient education
• Skilled nursing facility transitions
Mental Health • Care coordination for high-risk patients
• Assign a Primary Care Provider to each MH patient
• Eliminate delayed access
• Effective management of MH resources through patient prioritization
• Efficient patient transitions
Late Life Supportive
Care
• Redesigning care so that patient’s needs are documented and that caregivers including family
are able to access, understand, and comply during the course of caring for the patient
End Stage Renal
Disease (ESRD)
• Currently in process of reviewing potential opportunities with nephrologists

35. Results: Allina’s Elective Inductions
< 39 Weeks (%)
35%
30%
25%
20%
15%
10%
5%
0%
2009-01 2009-04 2009-07 2009-10 2010-01 2010-04 2010-07 2010-10 2011-01 2011-04 2011-07 2011-10 2012-01 2012-04 2012-07 2012-10 2013-01 2013-04
Allina Allina 2009 Baseline Allina 2013 Goal